Combined use of waist and thigh circumference to identify high‐risk, abdominally obese HIV+ patients
Bibliographic record
Abstract
Background We examined whether the combination of waist (WC) and thigh (ThC) circumference improves the prediction of visceral adipose tissue (VAT) over WC and ThC independently in HIV‐infected men and women after correction for age. We also examined the independent associations between VAT, and the combination of WC and ThC with metabolic risk factors, metabolic syndrome, type 2 diabetes mellitus (T2DM) and prior cardiovascular events in HIV‐infected individuals. Methods Consecutive patients attending the metabolic clinic of the University of Modena in Italy between 2005 and 2009 were recruited in this cross‐sectional study. Total and regional fat mass and lean mass were quantified using DEXA. A single CT image was taken for quantification of VAT and CAC. Prior cardiovascular events which occurred within a 5‐year period of the clinical evaluation were analysed. A cross‐fold test was used to explore different models in the ability to predict VAT in order to build an algorithm for VAT estimation (e‐VAT). Regression analysis were performed to determine the univariate and multivariate relations between WC, ThC, and age with VAT. A comparison of beta coefficients for VAT and e‐VAT to predict cardio‐metabolic risk and events were performed using multivariable regression models after correction for BMI and age. Results 2322 HIV‐infected patients were recruited: median duration of HIV infection was 182 months (IQR 126–236); median nadir and current CD4 were 172 (IQR 68–262) and 515.5 (IQR 369–700) and 75% of them had undetectable HIV1‐VL. In this abstract only the results of men will be presented. Men (n=1481) had a mean age of 45.9±7.3 years, a BMI of 24.1 ± 3.8 kg/m2, a WC of 88.0±10.1 cm and a ThC of 47.8±4.3 cm. e‐VAT algorithm for men was: (5.44*WC)−(1.35*ThC)−(1.70*age)−348.1 In men, at multivariable regression models after correction for BMI and age, e‐VAT was concordant to VAT in predicting HOMA, MetS Risk, prior cardiovascular events (OR=1.01), was better than VAT in predicting T2DM (OR=1.00) and CAC>10 (OR=1.01) but was worse than VAT in predicting TC/HDL and TG. Discussion We confirm that ThC is inversely associated to VAT after correction for WC. e‐VAT is a sensitive tool to predict VAT more accurately than WC and ThC independently. e‐VAT proved to predict cardio‐metabolic risks and events in men and women, qualifying this variable for a potential clinical use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".